Abstract
This paper discusses concepts of self-organized complexity and the theory of Coherent Infomax in the light of Jaynes’s probability theory. Coherent Infomax, shows, in principle, how adaptively self-organized complexity can be preserved and improved by using probabilistic inference that is context-sensitive. It argues that neural systems do this by combining local reliability with flexible, holistic, context-sensitivity. Jaynes argued that the logic of probabilistic inference shows it to be based upon Bayesian and Maximum Entropy methods or special cases of them. He presented his probability theory as the logic of science; here it is considered as the logic of life. It is concluded that the theory of Coherent Infomax specifies a general objective for probabilistic inference, and that contextual interactions in neural systems perform functions required of the scientist within Jaynes’s theory.
Highlights
Many forms of organized complexity have arisen in nature’s long journey from uniformity to maximal entropy
What is self-organized complexity? What are the capabilities and constraints of the various forms of inductive inference, e.g., classical versus Bayesian [1], conscious versus unconscious [2]? How is local reliability combined with holistic flexibility? What is context, and how can it be used? How can the information theory measures that have been applied to these issues be tested, and what do they contribute to our understanding?. Better formalisation of these issues is clearly needed, so I will first give a brief outline of some conceptions of self-organized complexity, and of the theory of Coherent Infomax which uses information theory measures to formalize these issues [3,4,5,6]
An obvious tension underlies the notion of organized complexity
Summary
Many forms of organized complexity have arisen in nature’s long journey from uniformity to maximal entropy. Biological systems have created diverse forms of adaptively self-organized complexity despite the ever present forces of noise and disorder This self-organization occurs in open, holistic, far-from-equilibrium, “non-linear” systems with feedback, which makes them highly diverse and hard to predict. Better formalisation of these issues is clearly needed, so I will first give a brief outline of some conceptions of self-organized complexity, and of the theory of Coherent Infomax which uses information theory measures to formalize these issues [3,4,5,6] This theory was initially developed as a theory of mammalian neocortex, but here the possibility of a broader relevance is considered. Objectives of probabilistic inference in self-organized systems will be briefly discussed
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